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CS566 – Semantic Web

CS566 – Semantic Web. Knowledge Management & Semantic Web. Παπαγγελής Μάνος , Κοφφινά Ιωάννα , Κοκκινίδης Γιώργος. Computer Science Department - UoC Heraklion 5 June , 2003. Overview. Introduction to Knowledge Management Knowledge Management Weaknesses

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CS566 – Semantic Web

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  1. CS566 – Semantic Web Knowledge Management & Semantic Web Παπαγγελής Μάνος, Κοφφινά Ιωάννα, Κοκκινίδης Γιώργος Computer Science Department - UoC Heraklion 5 June, 2003

  2. Overview • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  3. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  4. What is Knowledge Management (KM) • There is no universal definition of KM • KM could be defined as the process through which organizations generate value from their intellectual and knowledge-based assets • KM is often facilitated by IT • Not all information is valuable • Two categories of knowledge • Explicit - Anything that can be documented, archived and codified, often with the help of IT • Tacit - The know-how contained in people's heads Knowledge Management & Semantic Web

  5. Technologies that support current KM Systems • Knowledge repositories • Expertise access tools • E-learning applications • Discussion and chat technologies • Synchronous interaction tools • Search and data mining tools. Knowledge Management & Semantic Web

  6. KM System Weaknesses • Searching Information • Word keywords don’t express the semantics • Extracting Information • Agents are not abletoextract knowledge from textual representations and tointegrate information spread over different sources • Maintaining • Sustaining weakly structured text sources is difficult and time-consuming • Such collections cannot be easily consistent, correct and up-to-date • Automating Document Generation • Adaptive Websites that enable dynamic reconfiguration based on user profiles require machine–accessible representation of the semi-structured data Knowledge Management & Semantic Web

  7. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  8. Ontology-based KM systems • Methodology for developing ontology-based KM systems • Ontologies can help formalize the knowledge shared by a group of people, in contexts where knowledge has to be modeled, structured and interlinked • Distinction between knowledge process and knowledge meta-process • Two orthogonal Processes with Feedback Loops • Knowledge Process • Knowledge Meta-process Knowledge Management & Semantic Web

  9. The Knowledge Process (1/4) • Knowledge Creation • Knowledge Import • Knowledge Capture • Knowledge Retrieval and Access • Knowledge Use Knowledge Management & Semantic Web

  10. The Knowledge Process (2/4) • Knowledge Creation • Computer-accessible knowledge moves between formal and informal • In order to have knowledge in the middle of the two extremes the idea is to embed the structure of knowledge items into document templates Knowledge Management & Semantic Web

  11. The Knowledge Process (3/4) • Knowledge Import • Importing knowledge into KM system has the same or more importance than creating it • For imported knowledge, accurate access to relevant items plays an even more important role than for homemade knowledge • Knowledge Capture • Knowledge capturing refers to the way that knowledge items, their essential contents and their interlinks are accessed (OntoAnnotate) Knowledge Management & Semantic Web

  12. The Knowledge Process (4/4) • Knowledge Retrieval and Access • Typically through a conventional GUI • Ontology can be used to derive further views of the knowledge (e.g. Navigation) and additional links and descriptions • Knowledge Use • It is not the knowledge itself that is of most interest, but the derivations made from it • No single knowledge item can be useful, but the overall picture derived the total analysis Knowledge Management & Semantic Web

  13. The Knowledge Meta-Process (1/3) • Feasibility Study • Kickoff phase • Refinement Phase • Evaluation Phase • Maintenance Phase Knowledge Management & Semantic Web

  14. The Knowledge Meta-Process (2/3) • Feasibility Study • Identification of problems and opportunity areas • Selection of the most promising focus area and target solution • Kick off phase • Requirement specification • Analysis of input sources • Development of baseline taxonomy Knowledge Management & Semantic Web

  15. The Knowledge Meta-Process (3/3) • Refinement phase • Concept Elicitation with domain experts • Development of baseline taxonomy • Conceptualization and Formalization • Evaluation Phase • Revision and Expansion based on feedback • Analysis of usage patterns • Analysis of competency questions • Maintenance Phase • Management of organizational maintenance process Knowledge Management & Semantic Web

  16. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  17. A Framework for KM on the SW • Knowledge Capturing • Knowledge Repository • Knowledge Processing • Knowledge Sharing • Using of Knowledge Knowledge Management & Semantic Web

  18. Knowledge Capturing • Knowledge can be collected from various sources and in different formats • Four Types of Knowledge Sources • Expert knowledge • Legacy Systems • Metadata Repositories • Documents • Need for Knowledge Capturing Tools Knowledge Management & Semantic Web

  19. Knowledge Repository • Use of Relational Databases • Efficient storing • Efficient Access to RDF metadata • It is an RDF Repository like RDFSuite or RDF Gateway Knowledge Management & Semantic Web

  20. Knowledge Process • Efficient manipulation of the stored knowledge • Graph-based processing for knowledge represented in the form of rules • E.g Deriving a dependency graph Knowledge Management & Semantic Web

  21. Knowledge Sharing • Knowledge Integration of different sources (Knowledge Base) and its utilization • Realized by searching for rules that satisfy the query conditions • Searching is realized as an inferencing process • Ground assertions (RDF triples) and domain axioms are used for deriving new assertions Knowledge Management & Semantic Web

  22. Using of Knowledge • Finding appropriate documents is essential, but the derivation made of them adds value to KM applications • Composition of documents • Use of conditional statements • Conditional Statements leads to efficient searching for knowledge • Precondition-Action Knowledge Management & Semantic Web

  23. Proposed KM Framework Knowledge Management & Semantic Web

  24. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  25. Knowledge Representation • Knowledge should be expressed by explicit semantics in order to be understood by automated tools • Select schemas and express knowledge through them • Knowledge sharing,merging and retrieval are possible if the categories used in the knowledge representation are connected by semantic links, expressed in ontologies Knowledge Management & Semantic Web

  26. Elements of Knowledge Representation • Ontologies and Knowledge Bases • Ontologies are catalogues of categories with their associated complete or partial formal definitions of necessary and sufficient conditions • A knowledge base is composed of one ontology (or several interconnected ontologies) plus additional statements using these ontologies • Ontology Servers • Permit Web users to modify the ontology part of the KB • Knowledge within Web Documents • Permit the insertion of knowledge inside HTML documents Knowledge Management & Semantic Web

  27. Challenges of Semantic Web • Scale of information • The information found on the Web is orders of magnitude larger than any traditional single knowledge-base • Change rate • Information is updated frequently • Lack of referential integrity • Links may be broken and information may not be found • Distributed authority • Trust of knowledge is not standard because data are obtained through different users • Variable quality of knowledge • Knowledge may differ in quality and should not be treated the same Knowledge Management & Semantic Web

  28. Challenges of Semantic Web (cont.) • Unpredictable use of knowledge • Knowledge base should be task-independent • Multiple knowledge sources • Knowledge is not provided by a single source • Diversity of content • The focus of interest is wider • Linking, not copying • The size of information forbid the copy of data • Robust inferencing • The degrees of incompleteness and unsoundness must be functions of the available resources • Answers could be approximate Knowledge Management & Semantic Web

  29. Ontology • Processing and sharing of knowledge between programs in the Web • Definitions • Representation of a shared conceptualization of a particular domain • A consensual and formal specification of a vocabulary used to describe a specific domain • A set of axioms designed to account for the intended meaning of a vocabulary • An ontology provides • A vocabulary for representing and communicating knowledge about some topic • A set of relationships that hold among the terms in that vocabulary Knowledge Management & Semantic Web

  30. Ontology Driven KR • Knowledge sharing and reuse • Enable machine-based communication • Reusable descriptions between different services • No more keyword-based approach… • …but syntactic- and semantic-based discovery of knowledge • Hierarchicaldescription of important concepts and definition of their properties (attribute-value mechanism) Knowledge Management & Semantic Web

  31. Languages for KR • XML • RDF / RDF Schema • DAML+OIL • OWL Knowledge Management & Semantic Web

  32. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  33. On-To-Knowledge • On-To-Knowledge was a European project that built an ontology-based tool environment to speed up knowledge management • Results aimed were • Toolset for semantic information processing and user access • OIL, an ontology-based inference layer on top of the Web • Associated Methodology • Validation by industrial case studies Knowledge Management & Semantic Web

  34. On-To-Knowledge Architecture Knowledge Management & Semantic Web

  35. On-To-Knowledge Technical Architecture Knowledge Management & Semantic Web

  36. Tools Used • RDFferret • Combines full text searching with RDF quering • OntoShare • Storage of the information in an ontology and querying, browsing and searching that ontology • Spectacle • Organizes the presentation (ontology-driven) of information and offers an exploration context • OntoEdit • Inspect, browse, codify and modify ontologies Knowledge Management & Semantic Web

  37. Tools Used (cont.) • Ontology Middleware Module (OMM) • Deals with ontology versioning, security (user profiles and groups), meta-information and ontology lookup and access via a number of protocols (Http, RMI, EJB, CORBA and SOAP) • LINRO • Offers reasoning tasks for description logics, including realization and retrieval • Sesame • Persistent storage of RDF data and schema information and online querying of that information Knowledge Management & Semantic Web

  38. Tools Used (cont.) • CORPORUM toolset • OntoExtract and OntoWrapper • Information Extraction and ontology generation • Interpretation of natural language texts is done automatically • Extraction of specific information from free text based on business rules defined by the user • Extracted information is represented in RDF(S)/DAML+OIL and is submitted to the Sesame Data Repository Knowledge Management & Semantic Web

  39. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  40. Conclusion Remarks • Current Knowledge Management technologies need to be revised • There are some architectures of Knowledge Management Systems for Semantic Web but there are only few KM applications available • Knowledge Representation has to meet the challenges that Semantic Web poses • On-to-knowledge proposes a fine architecture on which KM systems for SW can be based Knowledge Management & Semantic Web

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